Today, the transport sector is responsible for nearly one-quarter of global energy-related direct carbon-dioxide (CO2) emissions and is a significant contributor to air pollution [1]. In the United States, the transportation sector has the highest share (28%) in the mix of green-house gas (GHG) sources [2]. Some of the more developed nations across the globe are now committed to improve the climate and air quality. Countries like China, Europe and the United States are front runners in introducing ambitions policies to incentivize the production and adoption of plug-in electric vehicles (PEV’s). Along with the expected benefits of PEV uptake, large scale deployment poses a challenge for the electric grid, especially at the distribution level, since the charging load of an PEV is substantial. This load is dependent not only on the characteristics of the PEV, but also on its use and charging habits of its user(s). Since a PEV can be directly plugged into the grid at any available point, which may be spatially anywhere in the utility’s service area, it is important to model its accurate use and charging behavior of the users. Having precise knowledge of the load profile, the utilities can have a better economic solution to balancing the supply and demand. In this dissertation, an agent-based model is developed that estimates the impact of charging load of PEVs on the grid. It is based on reasonably realistic diverse human behavior pertaining to day-to-day driving patterns and charging practices and their effect on each other. The model portrays the heterogenous, spatial and temporal nature of this load, which depends on the habits and the interaction among different agents. The model mimics the heterogeneity of choices made by human drivers and its effect on the charging choices of other drivers, which is an important element to consider when depicting human behavior. The model uses travel statistics of conventional personally owned vehicles (POVs) from the National Household Travel Survey (NHTS) conducted by the Federal Highway Administration (FHWA) across different states of the United States from 2016 – 2017. The travel needs are modified to incorporate the effect of EV’s limited range and charging time requirements. A modified GIS map of Collinsville, IL, is used to implement the spatial requirements of travel, with, which highlight exact load points. The agent’s travel and charging choices are modelled with heterogenous rules of engagement with the environment and other agents. Common psychological effects of limited range, long charging times, and range anticipation are applied heterogeneously to all agents to create a macro environment. The resulting charging load is superimposed on existing substation transformer load and voltage profile is analyzed to study the impact of different charging strategies and charging infrastructure availability. Different case studies are analyzed to investigate the effect of the aggregated load of multiple charging points in the respective service areas of the distribution transformers.

In his 1980 book, Mindstorms, Seymour Papert proposes using microworlds to help children learn mathematics like mathematicians. In a microworld like LOGO that is culturally rich in math, Papert claims that learning math can be as natural as learning French in France. Although the technology at the time was adequate, LOGO faltered due to improper implementation in the classroom. A newfound political interest in inquiry and computer literacy could breathe new life into Papert's vision. In contrast with the routinized approaches to introducing aspects of programming that, arguably, limited the trajectory for the implementation of programming in schools (Papert, 1980), this report explores what can and does happen in the first few minutes using a more open, student directed, approach to programming with high school physics students. A grounded theory approach led to connections with Vygotsky's Zone of Proximal Development. / text

Optimal Foraging Theory is a set of mathematical models used in the field of behavioral ecology to predict how animals should weigh foraging costs and benefits in order to maximize their food intake. One popular model, referred to as the Optimal Diet Model (ODM), focuses on how individuals should respond to variation in food quality in order to optimize food selection. The main prediction of the ODM is that low quality food items should only be accepted when higher quality items are encountered below a predicted threshold. Yet, many empirical studies have found that animals still include low quality items in their diet above such thresholds, indicating a sub-optimal foraging strategy. Here, we test the hypothesis that such ‘partial preferences’ are produced as a consequence of incomplete information on prey distributions resulting from memory limitations. To test this hypothesis, we used agent-based modeling in NetLogo to create a model of flower choice behavior in a virtual bumblebee forager (SimBee). We program virtual bee foragers with an adaptive decision-making algorithm based on the classic ODM, which we have modified to include memory. Our results show that the probability of correctly rejecting a low quality food item increases with memory size, suggesting that memory limitations play a significant role in driving partial preferences. We discuss the implications of this finding and further applications of our SimBee model in research and educational contexts.

Optimal Foraging Theory is a set of mathematical models used in the field of behavioral ecology to predict how animals should weigh foraging costs and benefits in order to maximize their food intake. One popular model, referred to as the Optimal Diet Model (ODM), focuses on how individuals should respond to variation in food quality in order to optimize food selection. The main prediction of the ODM is that low quality food items should only be accepted when higher quality items are encountered below a predicted threshold. Yet, many empirical studies have found that animals still include low quality items in their diet above such thresholds, indicating a sub-optimal foraging strategy. Here, we test the hypothesis that such ‘partial preferences’ are produced as a consequence of incomplete information on prey distributions resulting from memory limitations. To test this hypothesis, we used agent-based modeling in NetLogo to create a model of flower choice behavior in a virtual bumblebee forager (SimBee). We program virtual bee foragers with an adaptive decision-making algorithm based on the classic ODM, which we have modified to include memory. Our results show that the probability of correctly rejecting a low quality food item increases with memory size, suggesting that memory limitations play a significant role in driving partial preferences. We discuss the implications of this finding and further applications of our SimBee model in research and educational contexts.

First generation ethanol production has experienced rapid expansion but is now at a crossroads facing impending industry transformation. While Saskatchewan’s ethanol industry has benefited from demand and policy instruments that have guided substantial growth in recent years, changing policy and market dynamics present new challenges which are compelling the industry to adjust. This thesis examines three factors that are suspected to influence ethanol plant locational decisions. The development of an agent-based simulation model in this thesis will ascertain how transportation networks, market synergies, and subsidization influence location stability for an ethanol plant. The long term interaction of these factors is unknown, therefore do tradeoffs exist between these factors or is it conditional for all to be present? Modeling factors that affect location stability through an agent-based approach creates a dynamic framework to understand how location attributes impact an ethanol agent’s longevity. It was found that location stability is affected by an ethanol agent’s distance to both primary transportation networks as well as product markets. Surprisingly, distance to DDGS (dried distillers grain with solubles) markets, a low value by-product of ethanol production, has a profound effect on location stability. Policy instruments and industry subsidization are considered key ethanol development drivers and the surge in ethanol industry growth brought hopes of rural revitalization. In Saskatchewan, policy was developed to support small ethanol plants, those 25 Mmly (million litres per year) or smaller, aimed at increasing farmer investment and alternative markets for wheat. Measuring the effect of subsidization on location stability was fundamental to understanding how a post subsidized ethanol industry may look. The research found that subsidization of Saskatchewan’s ethanol industry dramatically affected economies of scale and location decisions, which left ethanol agents unable to compete in an increasingly competitive ethanol industry.

The theory of Agency, specifically that developed by Jesen and Meckling (1976), will be the subject of examination. Agency theory has been the subject of extensive research since its introduction in modern form by Jensen and Meckling (1976). The generality of the theory of Agency appears unquestionable and it has been widely adopted. Surprisingly, however, the model correctly predicts particular phenomena under investigation in only the simplest of instances, and even in the simplest of instances there are cases where the simple agency model has limited success. Possible reasons for this failure may lie in the assumed universalist foundation and in the common formulation regarding agent behaviour, that all agents are self-interested rationalists seeking to maximise their own utility to the disregard of their principal's interest. While the hypothesis of self-interested rationalism may be apt in some contexts it may be misleading or inadequate in others. This is especially so when the narrow interpretations of self-interested rationalism are used. Human beings are more complex in their totality than can be represented in any parsimonious model. This is particularly a problem when model predictions are not empirically supported. Aspects omitted in a model may be a source of the misfit between prediction and observation. An extended conceptualisation and reformulation of agent behaviour is presented. An approach is developed that addresses the context of agent behaviour, the socio-environment within which the agent interacts. The context particularly refers to the institutional affiliations and interactions that influence agent behaviour through their belief structure (i.e., their Belief-Desire-Intention, BDI, model of rational action). Through the use of an institutional framework contextual analysis is incorporated into the theory of agency and ultimately agent behaviour. This agent is termed a socio-environmental rationalist agent (SERA) which is contrasted with the self-interested rationalist (SIR) agent in the existing agency literature. This research utilises an object-oriented approach to develop a simulation of the extended conceptualisation and reformulation of agent behaviour. Simulations investigate agent behaviours and outcomes at the micro (specifically through individualised SERA and SIR formulations) and macro (specifically through a multi-agent SERA community formulation in the context of the EU financial accounting harmonisation process) levels. Netlogo is the simulation tool through which this is attained. The simulation demonstrates how alternative formulations of rationality lead to different outcomes and these differences are evident at both levels. Importantly the extended model has outputs that are more in tune with current empirical evidence. The analysis thus demonstrates the plausibility of the extended conceptualisation and reformulation and the need to incorporate the context of behaviour more fully within the analysis of the principal-agent relationship. Through this extended examination of agent behaviour further theoretical and practical insights regarding the understanding of agent behaviour, the principal-agent problem and relationship, multi-agent communities, and of business and society in general may be attained. This dissertation provides one step in advancing our fundamental understanding of the principal-agent problem. The scope and power of agency analysis can be substantially extended using the approach and methods outlined, particularly beyond that present in existing Agency research.

abstract: Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern. First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation. To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2018

No presente modelo em NetLogo, implementou-se um código onde patches genotipicamente homogêneos, reproduzem-se no mapa composto de 64 x 64 células. Buscam parceiros entre si, seguindo algumas orientações. O par reprodutivo deve estar dentro de uma determinada distância genética (G) e espacial (S). Estes parâmetros definem qual a máxima divergência genotípica permitida para a reprodução (G) e qual a distância espacial máxima entre dois possíveis parceiros reprodutivos (S). Além destes, o sliderM determina a probabilidade de ocorrer mutação nos genótipos resultantes das reproduções e A a amplitude, i.e., a quantidade de mudança sofrida pelo genótipo do agente. A princípio, geneticamente homogêneos, todos os indivíduos podem potencialmente formar pares. Contudo, com ocorrência de trocas genéticas e mutações, na formação da prole, aumenta-se a diversidade genética e há isolamento reprodutivo entre indivíduos. Obteu-se especiação dos agentes, ocorrência de corredor de fluxo gênico e mapa robusto de combinação de parâmetros. / In the present model in NetLogo, we implemented a code where genotypically homogeneous patches, reproduce in a map consisting of 64 x 64 cells. Seek partners among themselves by following some guidelines. The breeding pair must be within a certain genetic (G) and spatial (S) distance. These parameters define the maximum genotypic divergence which allowed for reproduction (G) and that maximum spatial distance between two potential reproductive partners (S). In addition, the slider M determines the probability of mutation in resulting genotypes and A the amplitude, i.e., the amount of change experienced by the genotype of the agent. Primarily, genetically homogeneous, all individuals can potentially form pairs. However, with the occurrence of genetic changes and mutations in the offspring formation, the genetic diversity increases and there is reproductive isolation between individuals. There were agents speciation, occurrence of genic flow pathway and robust map of matching parameters.

No presente modelo em NetLogo, implementou-se um código onde patches genotipicamente homogêneos, reproduzem-se no mapa composto de 64 x 64 células. Buscam parceiros entre si, seguindo algumas orientações. O par reprodutivo deve estar dentro de uma determinada distância genética (G) e espacial (S). Estes parâmetros definem qual a máxima divergência genotípica permitida para a reprodução (G) e qual a distância espacial máxima entre dois possíveis parceiros reprodutivos (S). Além destes, o sliderM determina a probabilidade de ocorrer mutação nos genótipos resultantes das reproduções e A a amplitude, i.e., a quantidade de mudança sofrida pelo genótipo do agente. A princípio, geneticamente homogêneos, todos os indivíduos podem potencialmente formar pares. Contudo, com ocorrência de trocas genéticas e mutações, na formação da prole, aumenta-se a diversidade genética e há isolamento reprodutivo entre indivíduos. Obteu-se especiação dos agentes, ocorrência de corredor de fluxo gênico e mapa robusto de combinação de parâmetros. / In the present model in NetLogo, we implemented a code where genotypically homogeneous patches, reproduce in a map consisting of 64 x 64 cells. Seek partners among themselves by following some guidelines. The breeding pair must be within a certain genetic (G) and spatial (S) distance. These parameters define the maximum genotypic divergence which allowed for reproduction (G) and that maximum spatial distance between two potential reproductive partners (S). In addition, the slider M determines the probability of mutation in resulting genotypes and A the amplitude, i.e., the amount of change experienced by the genotype of the agent. Primarily, genetically homogeneous, all individuals can potentially form pairs. However, with the occurrence of genetic changes and mutations in the offspring formation, the genetic diversity increases and there is reproductive isolation between individuals. There were agents speciation, occurrence of genic flow pathway and robust map of matching parameters.

No presente trabalho foi desenvolvida uma ferramenta para tornar o ensino do câncer na graduação mais efetivo. Tal ferramenta foi criada utilizando simulações multi-agente na plataforma NetLogo e conceitos gerais do processo de formação do câncer. O modelo que serviu de base para a criação da ferramenta foi o de Hanahan & Weinberg (2011). Inicialmente, para mostrar que a ferramenta é adequada para o ensino do câncer, foram definidos conceitualmente os Sistemas Complexos e o câncer e, em seguida, foi mostrado como estes se relacionam. Para desenvolver o presente trabalho, foram utilizadas pesquisas em dados secundários, entrevistas em profundidade e participação em aulas, palestras e seminários. O resultado desse processo foi uma ferramenta com diversas aplicações capaz de ensinar sobre o câncer através de muita interatividade e experimentação. Foi elaborada uma discussão sobre a problemática do ensino no Brasil e como isso afeta a aceitação de novas metodologias de ensino conforme a que é apresentada neste trabalho. Discutiu-se sobre o ensino do câncer no Brasil e a utilidade de ferramentas da área de sistemas complexos para tal. Para finalizar o trabalho, foram sugeridas algumas formas interessantes de se estender o uso da ferramenta desenvolvida na pesquisa científica, na clínica médica, no melhoramento de exames diagnósticos, entre outras / In the current work was developed a tool to increase the effectiveness of teaching cancer in undergraduate courses. This tool was built through multi-agents simulations in NetLogo platform and using general concepts of the cancer formation process. The tool is based in Hanahan & Weinbergs (2011) model. Initially, in order to justify the adequacy of the tool in teaching cancer, the Complex Systems and the Cancer are conceptualized and then, how they are related to each other. Secondary data research, in-depth interviews and participation in classes, seminars and lectures on relevant subjects were used to develop the current work. The result of this process is a teaching tool with many applications able to teach the student through much interaction and much experimentation. There is a discussion about the education in Brazil and how it affects new methodologies in class as the one presented in this work. There was also a discussion about the teaching of cancer in Brazil and the usefulness of tools from the field of Complex Systems for this teaching. To finish the work, some interesting forms of extension of this tool are suggested